ReviewPediatric research2023
The use of machine learning and artificial intelligence within pediatric critical care.
Review in Pediatric research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in pediatric intensive care units: current applications in sepsis management.World journal of pediatrics : WJP · 2026Pooled it
- The intelligent neonatal healthcare: a systematic review of machine learning architectures integrating the internet of medical things and blockchain.Frontiers in artificial intelligence · 2026Pooled it
- Machine Learning Using Electroencephalography Predicts Acute Cerebral Injury in the Pediatric ICU.Neurocritical care · 2026Article
- Leveraging machine learning for hemodynamic phenotyping in pediatric continuous renal replacement therapy-toward precision monitoring.Pediatric research · 2026Article
- Artificial Intelligence in Predicting Mortality Risk for Critically Ill Children in Pediatric Intensive Care Units: A Systematic Review.Health science reports · 2026Review
- Clinical Applications of Data Science and Machine Learning in the Pediatric Cardiac Intensive Care Unit.Pediatric cardiology · 2026Review
- Vital signs as biomarkers of early clinical deterioration in pediatric emergency departments: physiology, interpretation, and innovations: a narrative review.International journal of emergency medicine · 2026Review
- Data subdivision approach enhances machine learning-based mortality prediction in pediatric ICU patients.PloS one · 2026Article
- An interpretable machine learning tool for predicting perioperative cardiac events in patients scheduled for hip fracture surgery: insights from the multicenter LUSHIP study.Journal of anesthesia, analgesia and critical care · 2025Article
- Artificial intelligence in pediatric intensive care: unlocking integrated monitoring for autonomic nervous system dysregulation.Pediatric research · 2025Article
- Performance of DeepSeek and GPT Models on Pediatric Board Preparation Questions: Comparative Evaluation.JMIR AI · 2025Article
- Prediction of Postoperative Pain and Side Effects of Patient-Controlled Analgesia in Pediatric Orthopedic Patients Using Machine Learning: A Retrospective Study.Journal of clinical medicine · 2025Article
- Dynamic mortality prediction in critically Ill children during interhospital transports to PICUs using explainable AI.NPJ digital medicine · 2025Article
- Ethical and Practical Considerations of Artificial Intelligence in Pediatric Medicine: A Systematic Review.Cureus · 2025Review
- Role of artificial intelligence in pediatric intensive care: a survey of healthcare staff perspectives in Saudi Arabia.Frontiers in pediatrics · 2025Article
- Pediatric Oncology Hospice: A Comprehensive Review.The American journal of hospice & palliative care · 2024Review
- Acute Respiratory Failure in Children: A Clinical Update on Diagnosis.Children (Basel, Switzerland) · 2024Review
- Evaluating and Reducing Subgroup Disparity in AI Models: An Analysis of Pediatric COVID-19 Test Outcomes.medRxiv : the preprint server for health sciences · 2024Article
- Applied artificial intelligence for global child health: Addressing biases and barriers.PLOS digital health · 2024Review
- Use of artificial intelligence in critical care: opportunities and obstacles.Critical care (London, England) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The field of pediatric critical care has been hampered in the era of precision medicine by our inability to accurately define and subclassify disease phenotypes. This has been caused by heterogeneity across age groups that further challenges the ability to perform randomized controlled trials in pediatrics. One approach to overcome these inherent challenges include the use of machine learning algorithms that can assist in generating more meaningful interpretations from clinical data. This review summarizes machine learning and artificial intelligence techniques that are currently in use for clinical data modeling with relevance to pediatric critical care. Focus has been placed on the differences between techniques and the role of each in the clinical arena. The various forms of clinical decision support that utilize machine learning are also described. We review the applications and limitations of machine learning techniques to empower clinicians to make informed decisions at the bedside. IMPACT: Critical care units generate large amounts of under-utilized data that can be processed through artificial intelligence. This review summarizes the machine learning and artificial intelligence techniques currently being used to process clinical data. The review highlights the applications and limitations of these techniques within a clinical context to aid providers in making more informed decisions at the bedside.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.